Preparation of a new explainer is initiated
-> data : 16 rows 11 cols
-> target variable : Parameter 'y' was a pandas.DataFrame. Converted to a numpy.ndarray.
-> target variable : 16 values
-> model_class : sklearn.model_selection._search.RandomizedSearchCV (default)
-> label : XGBoost
-> predict function : <function yhat_proba_default at 0x0000019D94DEF700> will be used (default)
-> predict function : Accepts pandas.DataFrame and numpy.ndarray.
-> predicted values : min = 0.151, mean = 0.565, max = 0.923
-> model type : classification will be used (default)
-> residual function : difference between y and yhat (default)
-> residuals : min = -0.894, mean = -0.0651, max = 0.849
-> model_info : package sklearn
A new explainer has been created!
Preparation of a new explainer is initiated
-> data : 16 rows 11 cols
-> target variable : Parameter 'y' was a pandas.DataFrame. Converted to a numpy.ndarray.
-> target variable : 16 values
-> model_class : sklearn.model_selection._search.GridSearchCV (default)
-> label : Support Vector Machine
-> predict function : <function yhat_default at 0x0000019D94DEF670> will be used (default)
-> predict function : Accepts pandas.DataFrame and numpy.ndarray.
-> predicted values : min = 0.0, mean = 0.5, max = 1.0
-> model type : classification will be used (default)
-> residual function : difference between y and yhat (default)
-> residuals : min = -1.0, mean = 0.0, max = 1.0
-> model_info : package sklearn
A new explainer has been created!
Preparation of a new explainer is initiated
-> data : 16 rows 11 cols
-> target variable : Parameter 'y' was a pandas.DataFrame. Converted to a numpy.ndarray.
-> target variable : 16 values
-> model_class : sklearn.model_selection._search.RandomizedSearchCV (default)
-> label : Random Forest
-> predict function : <function yhat_proba_default at 0x0000019D94DEF700> will be used (default)
-> predict function : Accepts pandas.DataFrame and numpy.ndarray.
-> predicted values : min = 0.246, mean = 0.551, max = 0.898
-> model type : classification will be used (default)
-> residual function : difference between y and yhat (default)
-> residuals : min = -0.848, mean = -0.0505, max = 0.754
-> model_info : package sklearn
A new explainer has been created!
Preparation of a new explainer is initiated
-> data : 16 rows 11 cols
-> target variable : Parameter 'y' was a pandas.DataFrame. Converted to a numpy.ndarray.
-> target variable : 16 values
-> model_class : sklearn.model_selection._search.GridSearchCV (default)
-> label : Gradient Boosting
-> predict function : <function yhat_proba_default at 0x0000019D94DEF700> will be used (default)
-> predict function : Accepts pandas.DataFrame and numpy.ndarray.
-> predicted values : min = 0.0168, mean = 0.51, max = 0.962
-> model type : classification will be used (default)
-> residual function : difference between y and yhat (default)
-> residuals : min = -0.93, mean = -0.00993, max = 0.983
-> model_info : package sklearn
A new explainer has been created!